{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e07363f1-686c-4499-bd2b-115fb317cd3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b97d2e96-71b3-42e8-bf65-8d0bddcdc746",
   "metadata": {},
   "outputs": [],
   "source": [
    "x1 = np.linspace(-5,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f24aa9fc-653f-44f5-bf80-592c17bf1993",
   "metadata": {},
   "outputs": [],
   "source": [
    "function y1(x):\n",
    "    return 5*x1 + 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2c64bc0f-5dae-4efa-a2e3-636d4a5a6b07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xlim(-5,5)\n",
    "plt.ylim(-10,10)\n",
    "\n",
    "plt.gca()\n",
    "plt.plot(x1,y1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b23e06e7-6b87-4856-a4b7-f92a9b2e9195",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
